arXiv subjects

Komal Chawla

Publications and source records attributed to Komal Chawla.

5 recordsLinked to original sources

arXiv2025

Enduring mechanical memory from the constitutive response of elastically recoverable nanostructured materials

Mechanical memory and computing are gaining significant traction as means to augment traditional electronics for robust and energy efficient performance in extreme environments. However, progress has largely focused on bistable metamaterials, while traditional constitutive memory effects have been largely overlooked, primarily due to the absence of compelling experimental demonstrations in elastically recoverable materials. Here, we report constitutive return point memory (RPM) in elastically recoverable, vertically aligned carbon nanotube (VACNT) foams, analogous to magnetic hysteresis-based RPM utilized in hard drives. Unlike viscoelastic fading memory, VACNTs exhibit non-volatile memory arising from rate-independent nanoscale friction. We find that the interplay between RPM and frictional dissipation enables independent tunability of the VACNT dynamic modulus, allowing for both on-demand softening and stiffening. We leverage this property to experimentally demonstrate tunable wave speed in a VACNT array with rigid interlayers, paving the way for novel shock limiters, elastodynamic lensing, and wave-based analog mechanical computing.

cond-mat.mtrl-sci↗

arXiv2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our model performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

stat.AP↗

arXiv2025

Implicit Geometric Descriptor-Enabled ANN Framework for a Unified Structure-Property Relationship in Architected Nanofibrous Materials

Hierarchically architected nanofibrous materials, such as the vertically aligned carbon nanotube (VACNT) foams, draw their exceptional mechanical properties from the interplay of nanoscale size effects and inter-nanotube interactions within and across architectures. However, the distinct effects of these mechanisms, amplified by the architecture, on different mechanical properties remain elusive, limiting their independent tunability for targeted property combinations. Reliance on architecture-specific explicit design parameters further inhibits the development of a unified structure-property relationship rooted in those nanoscale mechanisms. Here, we introduce two implicit geometric descriptors -- multi-component shape invariants (MCSI) -- in an artificial neural network (ANN) framework to establish a unified structure-property relationship that governs diverse architectures. The MCSIs effectively capture the key nanoscale mechanisms that give rise to the bulk mechanical properties such as specific-energy absorption, peak stress, and average modulus. Exploiting their ability to predict mechanical properties for designs that are even outside of the training data, we propose generalized design strategies to achieve desired mechanical property combinations in architected VACNT foams. Such implicit descriptor-enabled ANN frameworks can guide the accelerated and tractable design of complex hierarchical materials for applications ranging from shock-absorbing layers in extreme environments to functional components in soft robotics.

cond-mat.mtrl-sci↗

arXiv2024

Embracing Nonlinearity and Geometry: A dimensional analysis guided design of shock absorbing materials

Protective applications require energy-absorbing materials that are soft and compressible enough to absorb kinetic energy from impacts, yet stiff enough to bear crushing loads. Achieving this balance requires careful consideration of both mechanical properties of the material and geometry of the shock-absorbing pads. Conventional shock-absorbing pads are typically made from very thick foams that exhibit a plateau of constant stress in their stress-strain response, while foams with a non-linearly stiffening stress-strain response are often considered ineffective. Contrary to this belief, we demonstrate that foams with a nonlinear stress-strain response can be effective for achieving protective pads that are both thin and lightweight, particularly for pad geometries requiring a large cross-sectional area. We introduce a new framework for the thickness or volume-constrained design of compact and lightweight protective foams while ensuring the desired structural integrity and mechanical performance. Our streamlined dimensional analysis provides geometric constraints on the dimensionless thickness and cross-sectional area of a protective foam with a given stress-strain response to limit the acceleration and compressive strain within desired critical limits. We also identify optimal mechanical properties that will result in the most compact and lightest protective foam pad for absorbing the given kinetic energy of impact. Guided by this design framework, we achieve optimal protective properties in hierarchically architected vertically aligned carbon nanotube (VACNT) foams, enabling next generation protective applications in extreme environments.

cond-mat.mtrl-sci↗

arXiv2022

Superior mechanical properties by exploiting size-effects and multiscale interactions in hierarchically architected foams

Protective applications in extreme environments demand thermally stable materials with superior modulus, strength, and specific energy absorption (SEA) at lightweight. However, these properties typically have a trade-off. Hierarchically architected materials--such as the architected vertically aligned carbon nanotube (VACNT) foams--offer the potential to overcome these trade-offs to achieve synergistic enhancement in mechanical properties. Here, we adopt a full-factorial design of experiments (DOE) approach to optimize multitier design parameters to achieve synergistic enhancement in SEA, strength, and modulus at lightweight in VACNT foams with mesoscale cylindrical architecture. We exploit the size effects from geometrically-confined synthesis and the highly interactive morphology of CNTs to enable higher-order design parameter interactions that intriguingly break the diameter-to-thickness (D/t)-dependent scaling laws found in common tubular architected materials. We show that exploiting complementary hierarchical mechanisms in architected material design can lead to unprecedented synergistic enhancement of mechanical properties and performance desirable for extreme protective applications.

cond-mat.mtrl-sci↗